3, ISBN: 9781849965286
ID: 203179781849965286
Robust control mechanisms customarily require knowledge of the system's describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependen Robust control mechanisms customarily require knowledge of the system's describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Michie and Chambers' BOXES methodology created a black box system that was designed to control a mechanically unstable system with very little a priori system knowledge, linearization or approximation. All the method needed was some notion of maximum and minimum values for the state variables and a set of boundaries that divided each variable into an integer state number. The BOXES Methodology applies the method to a variety of systems including continuous and chaotic dynamic systems, and discusses how it may be possible to create a generic control method that is self organizing and adaptive that learns with the assistance of near neighbouring states. The BOXES Methodology introduces students at the undergraduate and master's level to black box dynamic system control, and gives lecturers access to background materials that can be used in their courses in support of student research and classroom presentations in novel control systems and real-time applications of artificial intelligence. Designers are provided with a novel method of optimization and controller design when the equations of a system are difficult or unknown. Researchers interested in artificial intelligence (AI) research Automation, Technology, The BOXES Methodology~~ David W. Russell~~Automation~~Technology~~9781849965286, en, The BOXES Methodology, David W. Russell, 9781849965286, Springer, 03/12/2012, , , , Springer, 03/12/2012
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ISBN: 9781849965286
ID: 9781849965286
Black Box Dynamic Control Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. The BOXES Methodology: Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Algorithmus Intelligenz / Künstliche Intelligenz KI Künstliche Intelligenz - AI AI ( Künstliche Intelligenz ) Messtechnik Schwingbewegung - Schwingungslehre ( Schwingung (physikalisch) ) Schwingungslehre ( Schwingung (physikalisch) - Schwingbewegun, Springer-Verlag Gmbh
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ISBN: 9781849965286
ID: 9781849965286
Black Box Dynamic Control Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. The BOXES Methodology: Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Systems Theory, Control Black Box Control Engineering Control Algorithms Chaotic Transients Vibration, Dynamical Systems, Control Dynamic Systems Control CP0000 Artificial Intelligence (incl. Robotics) BOXES Methodology Control Structures and M, Springer London
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ISBN: 9781849965286
ID: 9781849965286
Black Box Dynamic Control Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. The BOXES Methodology: Robust control mechanisms customarily require knowledge of the system`s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. BOXES Methodology Black Box Control CP0000 Chaotic Transients Dynamic Systems Control Learning Systems Algorithms B Control Structures and Microprogramming Artificial Intelligence (incl. Robotics) Systems Theory, Control Algorithms Vibration, D, Springer London
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2012, ISBN: 1849965285
ID: 9781849965286
In englischer Sprache. Verlag: Springer London, This book introduces the BOXES methodology. It also presents a generic BOXES coefficient that makes the system almost completely application-independent, eliminating much of the a priori system knowledge currently needed for the method to be possible. PC-PDF, 226 Seiten, XXII Seiten, 226 Seiten, [GR: 9684 - Nonbooks, PBS / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik], [SW: - Anlagenbau Elektronik und Nachrichtentechnik (Kommunikationstechnik)], [Ausgabe: 2012][PU:Springer London]
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Author: | |
Title: | The BOXES Methodology |
ISBN: | 1849965285 |
Details of the book - The BOXES Methodology
EAN (ISBN-13): 9781849965286
ISBN (ISBN-10): 1849965285
Publishing year: 2012
Publisher: Springer London
224 Pages
Language: eng/Englisch
Book in our database since 26.06.2012 02:53:39
Book found last time on 16.09.2016 15:38:42
ISBN/EAN: 1849965285
ISBN - alternate spelling:
1-84996-528-5, 978-1-84996-528-6
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